app update

app.py

@@ -1,9 +1,133 @@
-#app.py
 
 import gradio as gr
+import clip,torch
+import requests
+from PIL import Image
+import numpy as np
+import torch
+import torch.nn as nn
+from io import BytesIO
+import urllib.request
 
-def greet(name):
-    return "Hello " + name + "!!"
+# https://hhp-item-resource.s3.ap-northeast-2.amazonaws.com/magazine-resource/magazine/20221017154717/jin._s2.png
+# girl bag skirt eye beauty pretty
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+from selenium import webdriver
+from selenium.webdriver.common.by import By
+
+    
+def test2():
+    driver = webdriver.Chrome()        #웹드라이버가 있는 경로에서 Chrome을 가져와 실행-> driver변수
+    
+    driver.get('https://www.hiphoper.com/')     #driver변수를 이용해 원하는 url 접속
+    
+    imgs = driver.find_elements(By.CSS_SELECTOR,'img.card__image')       #css selector를 이용해서 'tag이름.class명'의 순으로 인자를 전달
+    result = []     #웹 태그에서 attribute 중 src만 담을 리스트
+    
+    for img in imgs:                                #모든 이미지들을 탐색
+        # print(img.get_attribute('src'))         #이미지 주소를 print
+        result.append(img.get_attribute('src'))     #이미지 src만 모아서 리스트에 저장
+    
+    driver.quit()
+    
+    return result
+
+    
+def similarity(v1,v2,type=0):
+    if type ==0:
+        v1_norm = np.linalg.norm(v1)
+        v2_norm = np.linalg.norm(v2)
+    
+        return np.dot(v1,v2)/(v1_norm*v2_norm)
+    else:
+        return np.sqrt(np.sum((v1-v2)**2))
+
+    
+def democlip(url ,texts):
+    
+    if url =='':
+        print('SYSTEM : alternative url')
+        url = 'https://i.pinimg.com/564x/47/b5/5d/47b55de6f168db65cf46d7d1f0451b64.jpg'
+    else:
+        print('SYSTEM : URL progressed')
+        
+    if texts =='':
+        texts ='black desk room girl flower'
+    else:
+        print('SYSTEM : TEXT progressed')
+    
+    response = requests.get(url)
+    image_bytes = response.content
+    texts = list(texts.split(' '))
+
+    """Gets the embedding values for the image."""
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model, preprocess = clip.load("ViT-B/32", device=device)
+
+    # image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device)s
+    text_token = clip.tokenize(texts).to(device)
+    image = preprocess(Image.open(BytesIO(image_bytes))).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        image_features = model.encode_image(image)
+        text_features = model.encode_text(text_token)
+        
+        logits_per_image, logits_per_text = model(image,text_token)
+        probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+
+    word_dict = {'image':{},'text':{}}
+    
+    ### text 
+    for i,text in enumerate(texts):
+        word_dict['text'][text] = text_features[i].cpu().numpy()
+    
+    ### iamge 
+    for i,img in enumerate(image):
+        word_dict['image'][img] = image_features[i].cpu().numpy()
+    
+    ###################### PCA of embeddings ########################    
+    ## pca of text
+    tu,ts,tv = torch.pca_lowrank(text_features,center=True)
+
+    text_pca = torch.matmul(text_features,tv[:,:3])
+
+    ### pca of image
+    imgu,imgs,imgv = torch.pca_lowrank(image_features,center=True)
+
+    image_pca = torch.matmul(image_features,imgv[:,:3])
+
+    # return word_dict 
+    print(text_pca.shape,image_pca.shape)
+    return text_pca,image_pca
+
+
+
+def PCA(img_emb, text_emb,n_components = 3):
+    x = torch.tensor([[1.,2.,3.,7.],[4.,5.,3.,6.],[7.,9.,8.,9.],[11.,13.,17.,11.]])
+    # plz change data type to float or complex 
+
+    print(x.shape)
+    u,s,v = torch.pca_lowrank(x,q=None, center=False,niter=2)
+
+    u.shape,s.shape,v.shape
+
+    [email protected](s)@v.T
+
+    # torch.matmul(x,v[:,:3])
+    pass
+
+
+
+# NODE type
+
+# PCA type.
+
+# ELSE type. 
+demo = gr.Interface(
+    fn=democlip,
+    # inputs = [gr.Image(),gr.Textbox(lable='input prediction')],
+    inputs = ['text',gr.Textbox(lable='input prediction')],
+    # outputs='label'
+    outputs = [gr.Textbox(label='text pca Box'),gr.Textbox(label='image pca Box')]
+    )
+demo.launch()

requirements.txt

@@ -0,0 +1,17 @@
+#requirements.txt
+
+transformers
+torch
+
+
+peft
+loralib
+numpy
+pandas
+
+tqdm
+torchvision
+
+selenium
+clip
+